摘要
由一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于人工智能的最新研究结果已经发表。据NewsRx记者从印度德里发回的新闻报道，研究表明，“人工智能使用Mac Hine Learning(ML)和Deep Learning(DL)来分析数据。在这两种系统中，数据都集中存储。”我们的新闻记者从德里理工大学的研究中获得了一句话：“所涉及的数据可能是敏感的，泄漏可能会导致后果。处理私密数据的应用程序，具有关键结果，不能解决这种风险，称为数据敏感应用程序(DSA)。例如医疗保健、金融等。由于数据量大，DSA所需的数据无法集中存储，ML和DL技术遵循数据集中的方法，在处理与DSA经常相关的分散数据方面存在困难。Federated Learning(FL)承认分散的ED数据，并提供了一种更安全和有效的方法来分析这些数据。这促使以前不情愿的实体，如银行，合作获取各种数量的数据。大多数DSA转换为FL，本文深入分析了FL在DSA中的作用，为FL的研究和实现提供了分类依据，并对DSA在医疗和金融领域的覆盖工作进行了深入的分析，展望了DSA在非DSA领域的应用前景。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Artificial In telligence have been published. According to news originating from Delhi, India, by NewsRx correspondents, research stated, “Artificial intelligence employs Mac hine Learning (ML) and Deep Learning (DL) to analyze data. In both, the data is stored centrally.” Our news journalists obtained a quote from the research from Delhi Technological University, “The data involved may be sensitive and leakage may incur consequen ces. Applications dealing with intimate data, with critical results, cannot affo rd this risk and are termed Data-Sensitive Applications (DSA). Some examples are healthcare, finance, etc. The data required for DSA cannot be stored centrally due to large amounts, or isolated data islands. The ML and DL techniques followi ng a data-centralized approach have difficulties in handling the scattered data frequently associated with DSA. Federated Learning (FL) acknowledges the scatter ed data and provides a more secure and efficient way to analyze such data. This motivates previously reluctant entities like banks to collaborate for variety an d quantity of data. Most DSA transitioned to FL, but the migration is not withou t concerns. These include communication costs, heterogeneity, and malicious atta cks. In this paper, we deeply analyze the role of FL in DSA and provide a taxono my for the studies and implementations of FL. Then we provide an insight into DS A covering works in healthcare and finance. A glance is provided at attempts in non-DSA with possible DSA applications.”
NETL
NSTL